Systems and methods for sensing balanced-action for improving mammal work-track efficiency

ABSTRACT

An example system includes one or more sleeves, each configured for attachment to a leg and comprising a pressure sensor, an accelerometer and a magnetometer. A processor processes sensor signals from the pressure sensor, the accelerometer and the magnetometer to estimate action (A) and work (W) using event detections of peak stance and valley swing events associated with leg movement, for optimizing energy efficiency for maintaining a balance of the body leaning forward in a gravity pull-down force, with the periodic upward force of the sequential foot-thrusts with the toes.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of application Ser. No. 15/296,766,filed Oct. 18, 2016, which is a continuation of application Ser. No.14/073,826, filed Nov. 6, 2013, now U.S. Pat. No. 9,470,763, whichclaims the benefit of provisional application No. 61/723,132, filed onNov. 6, 2012.

Application Ser. No. 14/073,826 is also a continuation-in-part ofapplication Ser. No. 13/570,152, filed Aug. 8, 2012, which is acontinuation-in-part of application Ser. No. 12/805,496, filed on Aug.3, 2010, which claims the benefit of provisional application No.61/344,260, filed on Jun. 21, 2010; of provisional application No.61/344,026, filed on May 10, 2010; and of provisional application No.61/282,527, filed Feb. 25, 2010.

Application Ser. No. 13/570,152 claims the benefit of provisionalapplication No. 61/521,278, filed on Aug. 8, 2011; of provisionalapplication No. 61/556,365 filed on Nov. 7, 2011; and of provisionalapplication No. 61/617,424 filed on Mar. 29, 2012.

The contents of each of these applications are incorporated herein intheir entirety.

BACKGROUND AND SUMMARY

Unlike the typical motion analysis for external observation of bodymovement using video cameras and force plate measurements, i.e., ascurrently used in gait analysis for clinical human locomotion research,the plethysleeve technology (PST) as generally described in U.S. Pat.No. 7,610,166 and U.S. Patent Publication No. 2011/0208444 (the contentsof each of which are incorporated herein in their entirety) measuresinstead, instinctually driven internal leg-forces, using two strap-onbands around the lower body limb muscles. Note that ‘plethysleeve’refers to, for example, technology generally described in the '166patent and '444 publication, and not compressional fabrics currently inuse by sports runners.

As further described herein, the instrumentation of recreational runnersis a newer product technology involving a simplified type of gaitanalysis, primarily using arrays of sensors on the feet and upper bodyparts to locate relative motion for extracting gait parameters. But,since the muscle force measured by PST is generated from cognitiveawareness, it is similar to what might be derived from human sensing ofperceived force differences, as cues in dynamic motion that efficientlymoves one forward on a path. PST is modeled as a foot step placement inmaking a TRACK, and ‘falling-forward’ with gravity's pull to the nextstep, while maintaining stability in an upright posture by efficientappendage motion (e.g., non-translational motion or BALANCE). PSTincorporates Micro Electro Mechanical System (MEMS) sensors with RFintra-connectivity and onboard processing to automatically providelocomotion efficiency information. This force sensing ‘perception’ ismeasured in real-time and is efficiently distilled into accurateparameters automatically.

As described below by way of example and without limitation, one aspectof PST measurements is continuously monitoring important muscle activitywith pressure sensors in the sleeve band, such as during the swingphase, when typical gait analysis with Ground Reaction Forcemeasurements are absent. PST is like the internal view of driving a car,by turning the wheels and pushing the gas pedal, vs. watching the wheelsturn from outside with a video camera used in gait analysis. Here, PSTprovides a unique ‘signal’ of the full body dynamic, useful for medicaldiagnosis of deviations from normality in body function to avoidphysiological failures, in mental control disruptions to prevent injury,and in deviations from normality in the elderly due to hidden disease.The technology is self-powered, using smart, inexpensive RF-networkedsensor-components, being economically feasible and useful for groupactivities. PST scales across many event and trend time periods beyond astride cycle, being useful to many applications, by automaticallyproviding simple, situational assessments products fortrainer/therapists. Uses range from reducing recreational injuries,improving health care for the elderly, and improving sport performanceprediction and improvement using assessment feedback. This automatedlocomotion information extraction can be provided directly to theindividual user as performance and health feedback fromaudio-earbud/visual-wristwatch. Or, it can be provided to a trainer'sfield laptop, assessing teams of instrumented players, and also as anuploaded information stream to network reporting for remote assessments,and then finally being warehoused for database mining. This furtherimproves the locally specific cueing of information for the individualas it relates to a more global population. PST is also useful forrealtime, mission reporting of military combatants, forhealth-assessment as Balance distortion in gait, and with potential inGPS-denied navigation, by using Track placement as location changes toaugment inertial measurements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1a and 1b show Normal Gait Stance/Swing Time Periods in the R-L-RGRF Gait Cycle Stride.

FIG. 2 Shows a Spatiotemporal COG and Foot Dynamics for a Gait StrideCycle of Two Steps in Top and Side Views.

FIG. 3 Shows Modeled Interaction of Cognitive System Commands, ExecutingCalf Muscle Memory Pressures for L-Foot Step Force.

FIG. 4 Shows Eight Gait Cycle Periods in Stance/Swing Walking, Beginningand Ending at Initial Contact (IC) During a Stride Cycle.

FIGS. 5a and 5b show Gait Cycle Period Components Related to EnergyAbsorption and Generation in Running and Sprinting Relative to LegPositions.

FIGS. 6a and 6b reproduce an Earlier Plot of PST Tri-MEMS Recorded Datain Gait Walking/Running Examples for Collocated PCBs on the Sleeve.

FIGS. 7a, 7b and 7c show Simulated and Real Linear/Nonlinear GaitDynamics Examples in Time and Harmonic Spectral Amplitude/PhaseSynching.

FIGS. 8a and 8b show Modeled Running MuscleAction-Absorption/Work-Generation for Periodic-Stance/Aperiodic-Swing(1.3 cycles).

FIGS. 9a and 9b show N-Module Sleeve Measurements for a Single LegSupport and a Double Leg Support, with COG/COP Alignment DefiningBalance.

FIGS. 10a and 10b define the Lagrangian Energy, with KE/PE ChangesDuring Gait Cycle Relative to Phasing Differences in Walking andRunning.

FIG. 11 Shows Both Newton Force and Lagrangian Energy Descriptions inBalance and Track Dynamics of Modeled 3D Oscillation.

FIGS. 12a, 12b and 12c show Balance and Track Locomotion in Analogy to aPaddleball Toy for Ball Stability/Instability as Changes Affecting EachOther.

FIGS. 13a and 13b show Earlier PST Data for 10 Sec of Correlated L-R LegMuscle Pressures in Stance/Swing in Normalized Zero-Value, 10 GaitCycles.

FIGS. 14a and 14b show Correlated Running Pressure Measurements for L-RCalf Pressure in Walking (10 sec/8 cycles) and Running (6 sec/8 cycles).

FIG. 15 Shows PST Pressure Sensor Improvements from Earlier Designs withContinuous Flow and Distinct, Reproducible Events (2.3 sec).

FIG. 16 Compares Nonlinear Scaling to Remove Electronic Distortions ofHigh Fidelity PST Measurement of Gait Cycle Dynamics (8.3 sec).

FIG. 17 Shows Four Pressure Sensor's Data, Correlating over All FourLower Body Limbs, with Replication of Individual Muscle Patterns.

FIGS. 18a and 18b show Modeled Action &Work in 3D Plotting Alignment, asStride Correlation of Balance & Track Driving Action & Work.

FIG. 19 Shows Frequency of PST Events Relative to Periodic Time Trends(Micro to Mesa as 10 msec to yearly).

FIG. 20 Shows Multi-scaled Parameters for Applications Using SelfSynching Sampling in Temporal Integration of Tau-Lagged Correlation.

FIGS. 21a and 21b -1, 21 b-2, 21 b-3, 21 b-4 and 21 b-5 show Algorithmw/Realtime Hardware Architectures, Extracting PST Data Example MetricsUnder Parametric Event Selection.

FIGS. 22a and 22b show Close-up of FIG. 21 Data Patterns in IC Trends ofSwing and Stance Peak Levels (117 cycles).

FIGS. 23a, 23b and 23c show a Notional Circuit Design Approach Using a3-HF/LF Band Merge for 24 bit Accuracy in Pressure Feature Bands to 16kHz, and Calibrated Data Scaling Using Analytic Functions.

FIG. 24 Shows PST Sleeve Preproduction Video Gait Images, and PSTCalf/Thigh Sleeve Pairs Products for Watch/Laptop Information Displayand Networked Doctor/Trainer Access.

FIG. 25 Shows a Mechanical Design Used in a Preproduction Design forSleeve Fabrication, Electronics, and Assembly.

FIG. 26 shows eight cycle components of gait.

FIG. 27 shows example mid-fidelity PST measurements of stance/swingphase reversal adjustments.

FIG. 28 shows an example model of balance.

FIG. 29 shows balance defined by CM and ACM dynamics.

FIG. 30 illustrates an example gait proprioception model.

FIG. 31 illustrates a proprioception gait flow model.

FIG. 32 shows details of proprioception muscles involved in locomotion.

FIGS. 33a, 33b and 33c show example PST measurements in gait.

FIG. 34 shows details proprioception modeling.

FIG. 35 shows an example high fidelity PST with ground reaction forceover two gait cycles.

FIG. 36 shows a more detailed view of the cycle shown in FIG. 35.

FIG. 37 shows example PST data for calf/thigh muscles.

FIG. 38 shows other example PST data for calf/thigh muscles.

FIG. 39 shows example PST data event time scales.

FIG. 40 shows an example arrangement of a PST system.

FIG. 41 shows an example stand-alone PST device.

FIG. 42 shows example unit data analysis with RF connectivity.

FIG. 43 shows example unit data analysis with parametric control.

GLOSSARY

PST—Plethysleeve Technology

MEMS—Micro Electro Mechanical Systems

RF— Radio Frequency

GRF—Ground Reaction Force

R— Right

L—Left

ACL—Anterior Cruciate Ligament

IC— Initial Contact, e.g., heel strike

TO—Toe Off

EMG—Electromyographic (EMG) potentials

COG—Center of Gravity; also Center of Mass (CM)

ACM—About CM dynamics being motion around the body centered CM point

M—Total ‘point mass’ replacement location for CM force modeling

G—Earth's gravitational field vector

g—Earth's G field as acceleration

CP— Gait cycle time period

PE—Potential Energy, e.g., Mgh

KE—Kinetic Energy, e.g., ½M|v|² and ½I|ω|²

h—Relative vector height in PE for g (scalar distance from the ground)

t1, t2, t3, t4, t5—Sequential time markers in a gait cycle time periodsfor a stride

x1, x2, x3—Sequential positions in a gait cycle as space positions for astride

I— Moment of inertia for the angular motion of the upper body

v—CM vector motion velocity for KE computations

ω—About CM angular motion velocity vector

Hz—Hertz units for frequency

SRV— Stride-to-stride variability

IR— Infrared

EOM—Equation of Motion

B— Earth's magnetic field vector

A—Foot step force vector onto ground, as measured with GRF

P— PST sleeve pressure sensor voltage measurement (i^(th) indexing, asP_(i))

COP— Center of Pressure, a pointing vector (A) to the CM location fromthe ground contact

L—Lagrangian Energy defined as L=KE—PE

a—General vector notation for an accelerating force vector (F) on a massm; F=ma

Ab—Absorption

Gen—Generation

St—Stance

Sw—Swing

PCB— Printed Circuit Board

MAG—Magnetic sensors

GRAV—Accelerometer sensors of gravity

PRES—Pressure sensors

Hg— Mercury; in earlier patents for measuring pressure as a loop ofHg-filled, rubber tubing

2D—Two dimensional geometry

3D—Three dimensional geometry

Bx, By—Magnetic field components measured in a 2D XY plane

Gx, Gy—Gravitational field components measured in a 2D XY plane

FFT—Fast Fourier Transform for spectral analysis of time series data

τ—Scalar representing a time lag used in the time delay of a correlationcalculation

τ—Vector of torque, pointing usually from the intersection of two othervectors in contact

A—Action as the time integration of L

W— Work as the vector dot product path integration of a force vectorwith path distance vector dx

+L, −L—Notation for action and reaction in minimizing the Lagrangiantime integrations

RTC—Real Time Clock

LF—Low Frequency

HF— High Frequency

B&T—Balance and Track

A&W— Action and Work

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

The movement dynamics of mammals is a complex process of multiple limbsand muscles exerting forces to create forward locomotion. Much of thelower human leg motion is described in the dynamics of the gait cyclewith stance and swing phases, as sketched in FIG. 1 to define the threebody steps in a stride sequence, right (R)-to-left (L)-back-to-right(R). Many applications involving human sports performance assessment,physical therapy, lower body injury assessment, diagnosis, treatment,and recovery assessment (e.g., ACL injuries), stroke, Alzheimer'sdisease, etc., utilize Gait Analysis techniques that are dominated byvideo and treadmill/force-plate data collection in gait labs, followedby human analysis of the data. PST measures Balance and Track (B&T) ofhuman locomotion, beyond the simple gait analysis parameters from thestance phase, but without instrumented treadmills and force plates, orvideo cameras, because the sleeve acquires, along with stance phasedata, a unique measurement of the swing phase information without footsensors, and automatically produces a combined metric of locomotion fromadditional correlation between both calf sleeves. The discussion belowsummarizes a simplified gait analysis representation, in order to showthe uniqueness of the PST measurements, and its use in complexlocomotion applications.

Gait Cycle

The gait cycle is modeled for a person walking, shown in FIG. 1a , as ifin a video sequence, with numbered event periods 1 through 8, sketchedwith the first stance phase component as a time point (i.e., starting at0% of the stride cycle time), for the R-foot heel strike of:

-   -   1) initial contact (IC) beginning the stance phase with the        ground creating a Ground Reaction Force (GRF) to the body from        this supporting limb, involving specific pelvis, hip, knee, and        ankle joint positions; while these body locomotion positions        change, the following, three sequential components occur after        IC, for    -   2) loading response (from the downward body response pressure        from the ground, as a spring compression from the ‘falling’        energy absorption),    -   3) mid stance from single limb support, and    -   4) terminal stance, of heel-off, and then moving on into the        next component of    -   5) toe off(TO) that leads into pre-swing, for the next three        swing phase components of    -   6) initial swing,    -   7) mid swing, and finally,    -   8) terminal swing, just before the cycle begins again, to        complete the 8 gait periods.

The lower part of FIG. 1a , repeats this same motion as a circularsequence, with walking time periods spent in nominally 60% of the gaitcycle in the stance phase, and 40% spent in the swing phase. Comparingthe eight gait cycle periods, clockwise around the circle, thepercentage of time spent in part of the cycle is shown. The stance phasebegins with a double-support mode from L with R legs at IC (R-leg heelstrike), and then a single-support mode from just the R-leg at midstance (R, swing-L), moving back to the double-support from the R with Llegs, just before the toe-off (TO) component of the R-leg is at preswing(i.e., L-leg moving into stance IC). Here, begins the swing phase, withthe acceleration of the R-leg into the swing from behind the body andthen being moved forward, through the remaining cycle. Note that twofeet are on the ground in a double support stance, in the transferbetween the L-to-R foot in the beginning, and this is repeated for theR-to-L foot in the termination (IC to TO to IC). It is also instructiveto watch a video of human locomotion on a treadmill (e.g., FIG. 24 showsthe 8-component frames), because while these phases are fundamental togait analysis, humans have many other, eventful changes in locomotion,such as rotating the foot slightly for a particular step, onlydiscovered from human study of the gait videos.

In summary, walking locomotion is modeled as an eight time periodsequence for each leg spending 40% in the swing phase of one limb(synched with the other in single, along with double limb stancetotaling 60%), and then moves into stance phase while the previous limbmoves into swing. These percentages change to an increased swing withrunning.

Thus, lower body dynamics require considerable balance to maintaineffective locomotion in making a track as sequenced footprints placed onthe ground. Walking can be described as an evolving falling downprocess, while balanced on one leg, with a recovery by quickly movingthe other leg forward to catch the fall in the swing phase. The GRF inFIG. 1b is measured with gait analysis force plates, varying with vectorcomponents of loading and friction during the cycle, where the footforce pressure moves down the sole, as shown on the right side of thefigure (i.e., heel strike, flat foot to heel off, then toe off; shownalso in two foot pictures on a treadmill for heel strike and toe off),with the left side showing a GRF loading force, and a stance phaseGRF-dip during single limb support (while the other leg is swinging),and then during swing the leg shows a GRF=0. The GRF plot shows only 36%of this walking stance phase has both feet on the ground for stability.

Considerable research in gait analysis using force plates that measureGRF, as a three dimensional loading vector with friction components, isused to understand the body dynamics, and the dynamics of this forcedetermine the three dimensional momentum of the foot as well. The bodyvertical force exhibits the double peaked curve during the stance phaseof the gait cycle shown in FIG. 1; i.e., it increases from zero to 10%above the body weight during the stance phase, decreases to 60% of thebody weight during transfer, and then increases back-up again to 10%above, and then decreases back to zero at TO during the swing phaseinitiation of the other foot. There are over 20 muscles involved withthe gait cycle, including dominance within all phases from five: gluteusmaximus, gluteus medius, vasti, soleus, and gastrocnemius, withstability being brought from muscles that do not span joints andcontribute to the contact loading force, as measured indirectly with thePST pressure sensors around the thigh and calf of the lower body regionsdescribed in a later section. Electromyographic (EMG) potentials,measured using electrical probes inserted in the muscle tissue or placedon the skin, display further action of the muscles during the gaitcycle. This EMG data is the standard model for gait analysis of a forcerepresentation using GRF measurements to understand various musclecontributions.

Modeled Two-Legged Gait Cycle Dynamics

The gait cycle is shown more specifically in Side and Top Views in FIG.2 (with the same gait cycle numbers shown just below the “L-side View”legend at the top, and just above the “R-side View” at the bottom, asused in FIG. 1a ), in order to see L-R leg synchronization, includingthe up/down body motion of the center of gravity (COG, as Earth'sgravitational force vector G).

In FIG. 2, the G vector force is an acceleration g, on the body's centerof mass (M, abbreviated as CM), beginning at the lowest height (h)position for lowest Potential Energy (PE) “bounce-down” time t1 and x1(L-foot step) in heel strike for the first step (indicated as a top viewfootprint, gait cycle time period (CP) #1, with a gray circle indicatingbody height by the diameter as if it is at a distance away from theviewer). The stance PE “bounce-up” is drawn in the L-side View at thetop of the figure as an inverse pendulum, swinging in a half sinusoid,beginning at time t1 in the drawing, peaking at t2 (mid stance, CP #3,with PE increasing as the circle appears closer to the viewer), andreturning back at time t3 (toe off, CP #5, with a top view “toe-print,”beginning the aperiodic, nonlinear swing motion CP #5 to CP #8). Here,the R-foot is in heel strike at x2, time t4 for the second R-foot step(#7), with the Kinetic Energy (KE) maximized (CP #8) from the inertial(I) component of angular swing velocity (co) momentum (½Iω²), added tothe linear velocity (v) forward momentum (½Mv²). The third step in theleft foot sequence is the return of the L-foot to the new cycle as abeginning heel strike.

Muscle Brain Control Functionality

The model for locomotion is that of the cognitive brain processcommanding specific direction to engage groups of muscles in asynchronized completion of locomotion actions. It will be shown in theexample embodiments of the PST that the muscle groups appear to operatein a self-synchronizing manner, particularly in the running phase. In anexamination of the neurophysiological basis of adaptive behavior throughEEG measurements, Freeman has shown a mass action model for collectionsof neural “masses,” with time-space behavior in a feedback loop control,which includes limit or terminal cycles, from impulse drivenoscillations having characteristic frequencies from a periodic drivennature, or an aperiodic behavior at the sub-system levels. On a globalscale, these brain-commanded sequences are brain wave frequencies ofalpha (8-12 Hz), theta (3-7 Hz), beta (13-30 Hz), and gamma (30-100 Hz),which are steady state, self-sustaining activities, but show a veryshort spectral resolution, as an inverse square frequency roll-off fortemporal correlation. Freeman proposes the aperiodic activity asstochastic chaos, which is a “ringing” of limit cycle attracters. Onecan extend this model to dynamic locomotion muscle actions as beingimpulse driven, aperiodic behavior at the local level, which is globallymaintained in a more periodic control function based on the cognitiveintentions of the brain. Such behavior might arise from training as‘muscle memory.’

Hence, in gait analysis, one can see stride-to-stride rate variability(SRV), representing human walking locomotion as an interaction of thecentral nervous system in the neural functions of the brain, and theintraspinal nervous system with the mechanical periphery at the bonesand muscle levels, as a biomechanical model. This is aproprioceptionsense of locomotion, because there is a feedback from the limb tendons,muscles, and articular joints. However, kinesthesia is distinguishedfrom locomotion by excluding the sense of balance. Proprioception isconsidered a feed-backward perception by making post-action adjustmentswith 100 msec delays; however the feed-forward component for balance isalso postulated in proprioception, where it is used for more rapidactions based on a pre-action knowledge of the limb locations, such asused in placing the fingers on the nose during a sobriety test to bewithin 20 mm. Various training mechanisms can improve this balancesensing, such as juggling or standing on a wobble board, which isenhanced with the eyes closed. Thus, locomotion is a combination offootfall placement knowledge after steps occur, and a sense of balanceis used for the next footfall placement, creating a track motion. Gaitanalysis using IR stroboscopic photometry has shown that elderlysubjects had up to 20% reduction in velocity and length of stride (withstooped posture, faster cadence, and increased double limb stance) overyoung adults, and which also included reductions in toe-floor clearance,arm swing, and hip and knee rotations. This is a combined reduction ofcadence and stride that normally reduces the expenditure of energy,under the criteria of energy conservation. While this reduced action canbe considered that of a change in the neurological health of theelderly, this is why the combined determination of track and balance,when studying the conservation of energy in gait analysis, is criticalto avoid artificial effects from stiff joints or absence of breath inthe elderly (i.e., requiring a normalization within a variety of studiedgaits).

There are five basic temporal patterns in locomotion conditions, andwhen studied with four walking conditions (normal, kicking a ball,stepping over an obstacle, and stooping right and left while grasping anobject), using EMG muscle recordings from between 16 to 31 ipsilaterallimb and trunk muscles in a set of 8 subjects, results showed thatmuscle activation associated with voluntary tasks was eithersynchronized with the locomotion, or had additional activationssupporting a superposition model of compound movements. This complexitycan be modeled with nonlinear mathematics shown in multifractal andchaotic Equations of Motion (EOM), and exhibit periodic and aperiodicbehavior, which also exhibits irregular SRV, leading to falls in youngchildren.

Unsteady locomotion is a sign of poor integration of muscle functionwith whole body dynamics and neuromuscular voluntary control, wherefast-motion (e.g., running) depends more on local control that can bebest modeled with spring-mass dynamics, which creates stabilizationduring unsteady running from changes in terrain, lateral impulsiveperturbations, and changes in substrate stiffness. These stabilizationmodes might be based on initial conditions, as seen in chaotic models,where the conditions arise from proximo-digital (i.e., length of thehumerus) differences in limb muscle architecture, function, and controlstrategy. Nonlinear fractal exponent modeling for the data has supportedcorrelation with forced pace gait conditions (i.e., metronome pace)having similar fractal exponent values to Parkinson's disease.

There is also a feedback that compensates for length dependent neuralcontrol, using ground contact sensing from GRF, which cause aredistribution of energy by the distal muscles through their tendons.The optimization, of this energy use in locomotion, can allow mammals toachieve stability under a variety of conditions. Comparisons between GRFand kinematic (ultrasound) gait measurements of heel-strike and toe-offidentification show high correlation, with slight differences with gaitspeed. This basic locomotion biomechanics is a vaulting over stiff legsin walking and compliant legs in running, but further analysis of thesemodels with data requires a compliant leg for both, and shows that gaitis but one of many legged motion solutions accessed by energy and speed,and is useful in stable animal and robotic locomotion. Another elementof stability is in the use of a retraction of the swing leg throughrotation, just prior to contact with the ground, changing thespring-mass angle-of-attack in responses to disturbances of stance-limbstiffness and forward speed. Robotic studies of four-legged locomotionin simulated and real environments are optimized to minimize energy usein gait locomotion.

Locomotion Upper/Lower Body Dynamics

This gait cycle locomotion action by the lower body can be modeled as anaction of the body CM movement in the earth's gravitational field, G,while exerting angular momentum from the upper body motion through thepelvis, about the body CM, as an about center of mass (ACM) motion. TheACM angular changes were measured with respect to the Earth's magneticfield vector (B). Finally, the GRF of the foot thrusts, made during thegait stance as a transfer of CM weight between the two feet, and also asa balance of one foot, while the other foot was in swing, creates areactive force vector (A) in response to the Earth's force G. This is avertical pressure component, and two lateral shear components (shown inFIG. 1b as loading and friction). A pressure sensor can be used toactively measure these force amplitudes as determined by thecircumferential pressure changes at the lower body calf positions. FIG.3b shows these muscle size changes, measured by the PST sleeve shown onthe right calf as pressure P_(i) for each i^(th) muscle sensormeasurement (shown in the inset of a calf-muscle cross section). Thesemuscles are being commanded by the cognitive brain (FIG. 3a , e.g., 5 Hzrate), and exerted with ‘muscle memory’ training (FIG. 3a , e.g., 200 Hzrates), creating a foot thrust, step force vector (A) onto the ground.

Importance of Muscle Energy Absorption/Generation

FIG. 4, taken from a more recent description of walking, shows thecommon modeling of the gait cycle with a similar, sketched human walkingin the 8-definable dynamic periods, i.e., the stance phases defined herein the figure as LRT, MST, TST after foot touching the ground, IC, andthe swing phases of ISW, MSW, TSW, with the most important dynamic beingthe preswing, PS, which precedes the foot-thrusting toe off dynamic, TO.The walking gait cycle is contained within the two steps of IC-R, TO-L,IC-R, shown as swing to stance percentages at (37%/63%) respectively.Here, during the PS, the cognitive commands to the lower body muscles,are supplying a derivative of sensing upper body dynamics by the brainand perceived forward motion dynamics, to inform the brain how to createthe magic of swinging the lower body limb to re-engage the stance phaseof Track, again in Balance. Improper Balance creates problems in Track,and Track problems create improper balance. FIG. 5 is a rescaling ofFIG. 4, for running (FIG. 5a , upper) and sprinting (FIG. 5b , lower)dynamics, showing energy absorption and generation within the gait cycle(using abbreviations defined in the figure). There is also shown at thetop of FIG. 5, an overlay of the famous Muybridge still photographs of arunner, here cut into a different sequence to match up with the stickfigure drawings. The temporal cycle retains the IC-TO-IC gait cyclemarkers (with other markers; i.e., a reversal in stance being R-to-Lshown with feet and lower body muscle drawings, noted by StR, and areversal in swing being L-to-R, noted by SwR). These other markers haveplacement in time distinguishing between running (upper linearly markedbar) and sprinting (lower, linearly marked bar). Here, the swing tostance percentage ratio increases, for running as (38%/62%) andsprinting as (35%/65%). Notice also in the photographs, the elaborateextension and compression of the calf limb relative to the thigh limb,by the lower body muscles, beginning in the StR point, into just beforethe IC point occupying over 8-frames of the runner pictures (e.g., maybe11, with angular changes in degrees of roughly 180° to 300, and back toclose to 1700 at the end of 11 frames, or on the order of 700degrees/sec!).

However, in FIG. 5, the difference from walking, shows only 5-phases,which focus on the absorption (Ab) and generation (Gen) of the energybeing transferred by the leg forces within the gate cycle, indicated byAb/Gen in stance (St), i.e., 1StAb, 2StGen, and in swing (Sw), i.e.,3SwGen, 5SwAb, and the swing phase reversal, 4SwR, occurring right afterthe SwR marker. This modeling is closer to the representation in the'166 patent, which measures the muscle action with pressure sensing atmuscle locations circumferential around the PST band, shown in FIG. 6,and discussed in the next section. Specific sleeve sensors on individualprinted circuit boards (PCBs) measure the angular motion with amagnetometer (MAG), the gravitational forces with an accelerometer(GRAV), and the muscle forces with a pressure sensor (PRES), around thesleeve band, as indicated in FIG. 3b for the six sensor boards, locatedwith arrows around the calf muscle cross-section.

Sleeve Information from Correlation Metrics

The metrics derived from the Balance and Track PST measurements aredetailed in ('444 publication, '166 patent), citing figure numbers from'444 publication (3-FIG. notation), are made relative to previousbiomechanical models and measurements (3-FIGS. 6a-6e ), and an exampleof the modeled swing motion nonlinearity is also shown (3-FIG. 7). Thesensing technology is shown migrating from Hg loop pressure sensors tobands of MEMS (Micro Electro Mechanical Systems, 3-FIG. 9), with aspecific example of Left-Thigh and Left-Calf Hg loop data (3-FIG. 12),showing the correlated motion displayed in the FIG. 5 photographs of therunner, with both parts of the double inverse pendulum in motion. Thecombined sensor groupings (3-FIG. 9) are on each PCB placed on anangular location around the band (3-FIG. 14) for pressure relative tothe muscles (3-FIG. 15, 16), and magnetic and gravitational-accelerationforces (3-FIG. 18). Data measured over a few gait cycles are shown(3-FIG. 21, as pressure for walking and running, and in 3-FIG. 22, ascollocated 2D magnetic, gravitational (x, y) measurements). FIG. 6 showssimilar data, with point connection lines removed.

Notice in the top part of the FIG. 6, i.e., FIG. 6a with the dashed-linecircles, there is the usual increased pressure from the thrusting in thestance phase (StR through TO of FIG. 5), as a partial, linear sine wavestructure of the periodic inverted pendulum (upper left side, “PRESwalk”), which flattens at the top during running (upper right side,“PRES run”); also note in both walk/run examples the nonlinear motionwith a more narrower, valley shaped “sine” wave in the swing phase, withthe same half period as the stance, but as a more pointed dip. This isthe aperiodic nonlinear motion of the swing phase. The pressure is shownfor five PCB measurements, from pressure sensor (PRES) examplesP_(i)(i=1, 2, 3, 4, 5) being synchronously overlaid in time in FIG. 6a ,with four periodic stance phases of the single right leg calf sleeve, ashalf sinusoids in five-colors for different muscle positions around thesleeve (some have noise contamination). One can also see the individualmuscle measurement contributions changing within each cycle indicated bya dashed circle, where each muscle contribution to the gait cycle isslightly different in dynamics within the cycle, as if some muscles firesooner or later in the periodic motion, and also at different times inthe swing phase. This overlay is also apparent in the swing phase ofboth the walk and run data. The walking data is like a standard invertedpendulum model, but for running gait models, the body operates more likea “pogo stick,” with both feet off the ground at once in a swing phase,but landing on one at a time in the stance phase, thus showing moredifference in the plot through the swing “valley” for each muscle. Thesevariations with pressure sensor measurement around the sleeve can beuseful for a more refined characterization in locomotion, such as inpatients with hip or knee problems, or for a preference in footprintlay-down, caused by ankle issues. Pressure data differences ofneighboring boards in this data region can show a variation within gaitperiod, to monitor trends with the rotation of the limb (from B) incorrelation with variations in the muscle component contribution (fromP_(i+1)−P_(i)).

The two orthogonal sensor measurements of FIG. 6b (i.e., on the left forBx1 vs. By1 and Bx2 vs. By2; and on the right for Gx1 vs. Gy1 and Gx2vs. Gy2) are plotted as scattered point pairs in time, over the gaitcycles with the swing and stance phases indicated for both sets ofboards. Since during the stance motion, the leg is not moving much, theMAG location dots are then in a smaller sized group, as shown for threemarked stationary “stance” groups (there is also a fourth group for thefour stance pressure markings, grouped just under the “swing” labelingof the swing dot patterns). But in the swing phase, the MAG motiontraces out a relative angular pattern reproduced roughly as a retrace inthe same structure on each of the cycles shown. This dynamic motionpattern is the 2D projection from the 3D calf motion shown in theearlier runner photographs, which can be combined from the two boardsfor a 3D MAG angular location position. Similarly, the acceleration datais shown on the right side of the figure, with a similar tightclustering of a gravitational field force during stance, which thenexpands out as an increased gravitational force grouping, changing to alarger acceleration value, due to the centrifugal force accelerationchange during the swing state, causing an apparent “increased”gravitational force, pulling on the leg as it swings (i.e., like at aplayground, sliding out and holding with just one's hands, whilerotating on the merry-go-round table). Note also, that the pressureshows continuous data changes between swing and stance phases, withnonlinear peaked pressure reductions during swing, being correlated withincreased GRAV measurements. This indicates the centrifugal forcereduces the sleeve pressure, from decreased circumference during swing.A modeling of this dynamic stance/swing pressure is discussed next.

Force Pressure Simulation Model

A simulation model was developed that recreates the rounded up pressureof the stance, and the downward, narrowed, “valley” pressure of theswing, as shown in FIG. 7. Here on the left (FIG. 7a ) is shown a linearsinusoid dynamic for linear periodic motion in an inverted pendulummodel, with a stance phase delineated from a swing phase by a dashedline, assuming equal stance and swing time periods. This has a singularspectral peak in the power, shown in the FFT of the data in the secondline of this column (marked as two sided frequencies, with a Fourierphase transition at each peak shown in the third line). The nonlinearnature of the swing phase was created with a 12^(th) ordered sinusoidsynchronized to the fundamental, shown in the center (FIG. 7b ) as areplacement part to the linear data on the left side, in just the swingphase. Note that in the second and third rows, this data has addedharmonics from the nonlinearity, which also have phase transitionsconfirming the synchronization. Finally, on the right side (FIG. 7c ) isshown the same format as the center simulation model, but here real,aperiodic PST data is shown at the top, with a compression of the stanceamplitude ‘up’ from the dashed line, and as an expansion of the swingamplitude ‘down’ from the dashed line. Here, the Fourier analysis hassimilar harmonics and also with aligned Fourier phase transitions. Thisdata supports the model of a self-synchronization form in the swingphase of the gait cycle.

Energy Absorption and Generation During Muscle Force Exertion

The feature of the muscles in synchronization, as shown in the modelingof the ‘1-sec gait cycle’ in the simulation and data example of FIG. 7,can be examined further in terms of energy absorption from compressivemuscle force ‘springs’ and energy generation from expansive muscle forcesprings at a later time. FIG. 8 is a similar drawing to FIG. 5 in termsof the absorption and generation during the gait cycle. However, here inFIG. 8b the cycle is extended into a 1.3 stride time period (IC to TO toIC to TO), with an overlay of various muscle groups contributing to thelocomotion. The extra stance-phase time plot allows for these musclegroups to show continuity across the stance from ‘R-periodic’ on theleft side of the figure, to the ‘L-periodic and R-periodic’ on the rightside of the figure in a double support transfer. The FIG. 7 simulationis overlaid here as FIG. 8a for a 1.0 stride-time, and aligned with ICto IC time points. The solid sinusoid line crosses zero, as a dashedcurve to show the nonlinear swing phase, where this nonlinear componentmatches up perfectly to the zero-crossing transitions and what would bethe continuation of a linear periodic. Within the errors of thisgraphic, note that in this aperiodic phase, the nonlinear swing motionis arbitrarily occupying 50% of the cycle to show relationship of theR/L periodic stance phases with the aperiodic swing phase. Note also,that the swing leg, anatomy drawing shows motion from back-to-side-toforward of the other leg in stance as shown in the middle leg drawingsabout the swing reversal point (SwR, indicated in the bottom of thefigure with an arrow). This is the same indication to be shown in FIG.10 for a Lagrangian energy change. This motion, driven by the rectus andanterior tibial muscles, is not measured on a force plate in gaitanalysis, and has an enormous contribution to the Work and Actionefficiency. It is a means of generating energy for the later stancetransfer and handoff to the upper body angular momentum. Extraction ofcorrelation, as metrics of information, is described next.

Balance and Track Metrics

The metrics of balance and track are based on the application of thefoot force vector A, created from the pressure measurements of thesleeve, P, and the B vector location, as shown in FIG. 9. Here, the CMis at an M vector endpoint, relative to the sleeve location, shown as aband of rectangular MEMS boards, synchronously measuring the B, G,vectors, and the scalar Pressure (P) from the R/L calf-sensors. By usingMAG sensors for the angular orientation of the calf limb, relative tothe Earth's magnetic field lines (B′/B′) as the “shank angle,” this canbe combined with P to estimate the actual force vector A, as A=P(B′/B′).The definition for the Center of Pressure (COP) is vector A pointing up.In FIG. 9a , such as for a single foot support during swing,misalignment of A with G shows a vector torque exists to create anunbalance. In FIG. 9b , the COP, COG vectors are symmetrically alignedfor both feet shown as occurs in a double support stance. The L-R calfsleeve data thus estimates Balance as a miss alignment between COP andCOG, and during single limb stance, angular momentum conversationsustains balance with an offset vector (mean ACM motion over a gaitcycle is not zero, without another contribution for Balance). Byexamining both legs together, the gait cycle is simplified with dynamicsas the single foot (swing) in ACM≠0, and double foot (stance) withACM=0. Dynamic stability is defined by the equations at the bottom ofFIG. 9. Changes in Track placement as a foot rotation can also be usedin countering ACM torque, as recently observed by Oscar Pistorious (anamputee runner with artificial legs made from flexible staves, as theCheetah Flex Foot product,” aka ‘The Blade Runner’), making Olympic 2012history, by placing 2^(nd) in a qualifying 400m race.

On the other hand, the Track metric can be estimated by the uniformityof the foot path placement estimated from the calf rotation swingcomponent when the gravitational vector angle is aligned with the shankangle at maximum pressure during the TO part of the gait cycle. Togetherwith balance, and the temporal identification of the eight-component,time periods of the gait cycle, a continuous estimate of Track andBalance can be made, based on synchronized MEMS sensor data estimatesfrom the sleeve pair. However, the efficiency of Balance and Track canbe estimated using the Lagrangian energy and force measurements for eachsleeve, based on the space-time changes in the relative two interactionforce vectors (G, A) detailed in Equation L3 for the Lagrangian energy(L), and the EOM for the torque vector (t). The definition of L is givenin FIG. 10, which also shows variation of KE and PE components inwalking (FIG. 10a ) vs. running (FIG. 10b ).

A more detailed description in the force diagram is shown in FIG. 11 forNewton (FIG. 11a ) and Lagrangian (FIG. 11b ) descriptions. Here, the KEand PE terms in L are defined with the inertial moment of ACM dynamics(a is the acceleration of the reactive force A on the mass M), and thecoupling of the ACM to the CM forward translation is represented in atwisting motion from the upper body limb cycles, connecting through thespine to the lower body pelvis, where the limb swing lengths are pumpingenergy into the dynamics, as a parametric amplifier. This correlatedenergy transfer in time is an integration of the Lagrangian energy(Action), used as an efficient form of energy transfer for making theapplied force change body limb positions in locomotion (Work), definingthe muscle efficiency as an algorithm using PST data discussed next.

Action and Work Using Balance and Track Representations

The description of the Lagrangian in FIGS. 10 and 11 indicate that thedynamics of the locomotion with the upper body motion during the stanceand swing phase transitions will have a major impact on the Balance andTrack, through the transfer of angular momentum. There is also a utilityin this representation, to estimate the Action and Work, defined in theequations below, using the gait cycle representations of FIG. 2. Here,Action (A) integrates L over time, in synchronous timing with the gaittime periods shown for the five time points of FIG. 2 (t1, t2, t3, t4,t5). Note that the integrations interweave the L for the total bodydynamics of both lower legs, and achieve a representation of the forcesfor Balance shown in FIG. 9b . Also shown below is the definition ofwork (W), being the integration of the two vector forces, as separatecomponents in the sleeve measurements, being integrated over the vectordot product of the infinitesimal motion vector direction, dx, with thevelocity v estimated from the time derivative sampled integration ofvelocity (v=dx/dt). The vector integration with a dot product (“⋅”) todx, is between the stance steps (stationary foot placement in Track),thus allowing for a negative sign between the two components, implyingthat the A and G vectors are in balance in a double support stance (FIG.9b ) without much forward motion, and are within the changingintegration dynamics between the three steps (x1, x2, x3) in the swingphase (FIG. 9a ), in a singular support stance (L in x2 to x3, R in x1to x2).

Action = ∫_(t 1)^(t 5)Ldt ≈ ∫_(t 1)^(t 4)(KE_(Right) − PE_(Left))dt + ∫_(t 4)^(t 5)(KE_(Left) − PE_(Right))dt${Work} = {{{\underset{\int{B{(x)}}}{\int_{x\; 1}^{x\; 3}}{G\; \bullet \; {dx}}} - {\underset{\int{B{(x)}}}{\int_{x\; 1}^{x\; 3}}{A\; \bullet \; {dx}}}} = {{\underset{\int{B{(x)}}}{\int_{x\; 1}^{x\; 3}}{\left( {G_{Right} - A_{Right}} \right)\bullet \; {dx}}} + {\underset{\int{B{(x)}}}{\int_{x\; 1}^{x\; 3}}{\left( {G_{Left} - A_{Left}} \right)\bullet \; {dx}}}}}$

Within this frame work of balanced Action and Work, one can compute thelocomotion efficiency of the Action being minimized under the Principleof Least Action. In the example of walking and running shown in FIG. 10,the out of phase requires a minimum energy lost in the stance exchangebetween double and single support, and is more critical in the stancereversal region indicated on the right side of the figure.

This review describes how computational modeling can be combined withnoninvasive gait measurements to describe and explain muscle and jointfunction in human locomotion. Five muscles—the gluteus maximus, gluteusmedius, vasti, soleus, and gastrocnemius—have been indicated tocontribute most significantly to the accelerations of the center of massin the vertical, fore-aft, and medio-lateral directions when humans walkand run at their preferred speeds. Humans choose to switch from a walkto a run at speeds near 2 m/sec to enhance the biomechanical performanceof the ankle plantar flexors and to improve coordination of the knee andankle muscles during stance. Muscles that do not span a joint cancontribute to the contact force transmitted by that joint and thereforeaffect its stability. In walking, for example, uniarticular muscles thatcross the hip and ankle act to create the adduction moment at the knee,thereby contributing to the contact force present in the medialcompartment. Many of these muscles are sensed within the placement ofPST sleeves on the limbs.

The example systems and methods described in this application relate tothe automation of the general field of determining mammal locomotionmetrics, from a simple viewpoint when muscular-driven support memberspropel the body, being that of linear momentum relative to the ground orother surfaces, defined as Track-movement, and being that ofangular-momentum relative to the body, defined as Balance-movement. Thisis uniquely different from gait analysis because these measurements aremade by totally self-contained, strap-on-bands that can be worn in anytype of locomotion activity including sports, and also by other mammals,such as horses, and does not require human analysis of any collecteddata. The example systems and methods incorporate band sensors worn onbody limbs with networked RF connectivity to compute, using relatedsensor data and fundamental physical models, muscular motion acrossmultiple band links and within a group of interacting sports players orracing mammals.

The particular sensing described in these measurements relate to theefficiency-of-retaining a Balanced-action of the upper-body angularmomentum during Track-movement, which switches between the two lowerbody limbs, where previously A is defined as the temporally integrated,expressed Lagrangian energy, and also in the efficiency-of-moving thelimbs forward during the placement of the foot, as a work Track-force.Here, W is defined as the actual force being integrated, over thespatial transition-distance of the limb, being moved between the forcesof gravity and muscular applied thrusting and extending forces (A), asmeasured by the combined band sensors worn on the body limbs, beingapplied for the next periodic track foot-step. Because this real-timemeasurement and monitoring is being made with a very high fidelity, andis made outside the laboratory in the world of more natural activities,the Track and Balance motion viewpoint allows the measured informationto be used in physical and mental health assessment. The metrics are ina database format for easy long-term trend analysis and populationdemographic characterization. Examples include use in sports training,in therapeutic injury-recovery monitoring (e.g., from either a predictedpotential-injury diagnosis, or form post-disorders and post-injuryrepair assessment), and in general health care and treatment of theelderly. This discussion follows, with a focus on the unique viewpointof Balance and Track, within the previous discussion of typical GaitAnalysis.

Gait Analysis—Placing Feet on a Track

The mammal process of upright locomotion has been characterized fordecades with gait analysis using measurements from feet striking forceplates during video recordings, being made in simplistic dynamics, suchas walking on a treadmill. The physical modeling of forward locomotionis part of biomechanics engineering, using complicated muscle and bonestructure anatomy with Newtonian force interaction representations, tocharacterize the changes from stand-still, to walking, running, andsprinting (at maximum speed), by creating a lower body activity,step-sequence of right (R) and left (L) foot placements used to make aTrack. As is well known from early horse racing pictures, running isdefined as having periods where all feet are off of the ground. Themotion is of the body mass center, rising and falling in a periodiccadence between the R-foot on the Track in the stance phase, and thenthe Balance of the upper body, to transfer the body mass weight to theplacement of the L-foot on the Track ahead of the first step. A finaltransfer of weight back to the R-foot with a second step completes thetwo-step gait cycle in time, as a stride of stride-length, at a speed,defined by this length and time, within a two legged, spatiotemporalcorrelation. These descriptions of Balance and Track use an analyticrepresentation in Lagrangian and Newtonian representations for thephysical modeling.

The foot placement track dynamic shown in FIG. 12a , is similar toplaying with an inverted toy paddle ball shown in FIG. 12b to make ananalogy, as a two-step gait cycle of stance shown on the right side inFIG. 12 c:

-   -   Track is the motion sideways, in position of the paddle and in        angle relative to the normal gravitational inclination, and    -   Balance is the ball position relative to the center point        directly above the paddle.

Gravity is the force applied by the rubber band in pulling the ball downto the paddle, and the foot-thrust to move the body mass to the otherfoot, is the paddle hitting force that drives the ball back up into theair. The shadow of the runner's feet positioning in FIG. 12c shows onlyone, stance foot touching the ground, and the other foot is in a swingphase off of the ground.

Efficient motion is when the ball stays in one position moving up anddown in a linear periodic motion directly above the paddle, using abiomechanical model of an inverted pendulum component during the stancephase, oscillating periodically from the ankle/foot-toe, staticposition. With the knee also being a recognized joint in this modeledmotion, this is called a double inverted pendulum. Finally, because thefoot placement of the body weight acts like the absorption of motionmomentum in compressing a spring when striking the Track, and there-generation of this absorbed momentum acts like the release of thecompressed spring's energy, the model includes a spring for absorbingand generation phases of momentum under conservation. This actioncreates a change in the circumferential pressure of the calf, which ismeasured with the PST sleeves, shown as an inset to FIG. 12a (and FIG.3a ), where each calf has a circumference-closure band, and the multipleMEMS pressure sensors are sensitive to individual muscle groupexpansions/contractions. The change, between stance and swing shown inFIG. 12c , is as if a second paddle hit the ball back into balance, thusindicating the importance of the momentum transfer by the swing phasefoot placement for the next track position for stance. Note also that ifthe paddle is tilted relative to the gravity pull straight down, theball will be moved out of balance, and can only be corrected by the next‘Track paddle hit’ at an opposite tilt.

An informal analysis of human locomotion is to compare the differencesbetween a baby crawling (all four limbs making tracks) and a footballplayer running (usually with one or no feet on the ground). Traininghumans to move more efficiently and to stay healthy has enormousbenefits; in the PST, the goal in these two comparisons is to move in anupright stance at a faster, safer pace, where the sleeve leg pressuremeasurements are translated into how one moves, and for professionalathletes, effected information is from measured changes in hundredths ofa sec increments. Inefficient movement develops fatigue, creatingstepping errors, inviting a poor cadence in stepping that is anunbalanced motion. This can create injuries; hence, the desire to moveupright vs. the inevitable action of falling down.

Thus the human cycle of forward motion is about the dynamics in dailylife, through exercise and sports, where dynamic errors cause injuriesand out of the ordinary changes can be precursors of mental changes too.The locomotion of placing feet on the ground to move forward is thehistoric “1 sec” gait cycle, measuring pace, cadence, step-length,step-rate, speed, and stride-length, where improper dynamics have aninefficient gait. The PST is making a unique and previously unavailablemeasurement. An interesting way of understanding these changes is tolook at images of humans in activities with zero, one, or two feettouching the ground:

TWO FEET Extended force—When stationary, we stand on two feet, ortransfer energy between feet when moving or swinging a club, racket,bat, etc. for applying an extended body force, which in many instances,this applied force is while on one foot, in such sports as tennis, golf,cricket, lacrosse, baseball, hockey, etc.

ONE FOOT Changing mass direction—The Newtonian physical modeling relatesthe “hitting” force (F) while moving to creating changes in the mass (M)direction, as an acceleration (a), which in turn reacts back as anunbalancing force to the human dynamics; this is where the Balance isperturbed, and thus perturbs the Track when the feet return to theground.

ONE FOOT Applying pushing force—The return of one foot to the groundmust include a landing of the body force, combined with the angularmomentum carried through the limb contact, which is usually referred toas a turn, cut, etc., which changes body motion direction as anextended, “pushing” force to keep balance with tracks in a newdirection. Here, basketball, football, soccer, rugby, and other contactsports involve extending forces through the body to catch balls in theair, push balls in the air towards a hoop or another player, or changedirection to avoid another player.

ZERO FEET Regaining Balance on return to ground Track-Finally, there arebody dynamics of being without any ground contact, such as throwing aball while in the air, aligning the limbs after leaving a ski jump, ormaneuvering on a snow board in the air, which all create a change inangular momentum of Balance, which must be transferred in an unknownmanner back to the Track upon contact with the ground.

Even video action gamers, jump and move in simulated actionenvironments, and elderly walk and run in low contact environments, witha muscle control being guided under a brain dynamic of requests toengage multiple muscles in creative unity of purpose. These actionsbenefit from Balance and Track measurements in enhancing the bodydynamics to the game feedback, or to monitor the body dynamics forinternal mental changes in health.

Gait Analysis—Swinging Feet in Balance

Just as important in the gait cycle of the stance phase, is the other,lower body action, which “magically” moves the back foot off of theTrack, and places it ahead of the other foot in the stance phase, justin time before the upright mammal falls over as the transfer of weightin the stance phase begins again. This is the stance compliment phasecalled the swing phase, which is not periodic, and is referred to asbeing “aperiodic.” While it is easy to refer to this as meeting aphysical argument of conservation of upper body angular momentum, theswing phase is anything but a simple, nonlinear action, and is not onlynot well modeled, but it is also not well measured in the video gaitanalysis sequences, because multiple cameras are required to describethe 3D motion of the swing leg as it moves back to the stance phase.

Current wearable devices used in gait measurement and recreationalactivities produce simple data recordings of external forceapplications, analyzed along with video by a human, to infercharacteristics of orderly body limb movement and symmetry, usingextensive biomechanical simulation models, but generally without anyinternal force sensing. The sleeve described herein is used in pairsthat correlates motion of both feet through the entire gait cycle andprovides information on Balance efficiency in the use of energy dynamictransfer as Action and in Track placement efficiency using the angularmomentum of the upper body Balance as Work in lifting and placing feet.An important element of Balance & Track, is not just the stance phase,but more importantly in the swing phase, used to adjust the momentum toreduce force errors from the GRF in re-establishing the next Track.

Sleeve Information Integrated from Pressure Sensor Measurements

A key point of the developed sleeve is the manner in which the humanlocomotion utilizes energy in achieving efficient work within the gaitcycle. This replaces conventional, external gait metrics of force platedata, video cameras, and biomechanical models, with onboard the body,energy and force information from Action and Work computations. Here thegait cycle is just a model of what really happens, to better categorizewhat is measured with the sleeve sensors. The points for integratedsensor data measurements to produce informed guidance and monitoringrequires a precise segmentation of the data as follows:

-   -   Gait dynamic characterization exists between a two-step, L-R-L        sequence of three-ICs, as the gait cycle, with units of:        -   1. Gait Speed (time to walk at preferred/quick speed for 20            ft), varying with age (20's to >80's, or frail) and sex from            1.18/1.97-3.57/6.4 (ft/sec) for men and 1.38/1.57-3.47/6.43            for women.        -   2. Stride-length (1.5 m), step-length (L-heel to R-heel),            step-rate (120 steps/min, which is an average speed of 1.5            m/sec stride-rate) are various distances and clocked time as            measured by calibrated photographic recordings of IC            placements for a set of footsteps while walking and running,            usually performed on a treadmill.        -   3. Three beat, two-step sequence as a cadence in stride,            related in mammal locomotion to oxygen uptake and fatigue,            with optimal locomotion efficiency, being most efficient at            a moderate walking pace.    -   PST mammal dynamic information, with units of:        -   1. Balance symmetry averaged over multiple gait cycles as a            continuous information output derived from cross leg-stance            asymmetric behavior.            -   Two-limb correlation analysis for locomotion information            -   Optimum conservation of angular momentum in Balance            -   Inefficient and abnormal in Balance is asymmetric, or                unBalance            -   Track footpath placement errors must be corrected by the                swing back to IC, for ‘re-spinning’ the angular momentum                wheel, to be in synch with Balance        -   2. Stance and swing as smooth transitions between periodic            and aperiodic muscle pressures derived from lower body            muscle force exertion and relative L to R muscle            correlations.            -   Sleeve pressures in calibrated sensors change about a                mean value between stance and swing phases of cadence            -   Periodic and aperiodic time periods have statistical                discrimination, with individual gait cycle corrections                for Balance, and efficient foot step placement to                maintain efficient Track        -   3. Periodic and aperiodic dynamics of cycle change forces            and energy use effecting locomotion efficiency.            -   Muscle forces change during moving limbs in gravity over                distances producing work (W) as the lower body muscles                lift and swing the legs after thrusting (A) during TO                leaving the supporting gravitational force (G), and                StR/SwR absorption/generation of linear momentum (spring                in the inverted pendulum).            -   Work integrates the force over spatial distance in                moving the feet into producing tracks as a Work-Track                from (A-G) changes (misaligned vectors have less work                from the reduced, projected aligned component).            -   Action (A) integrates the Euler-Lagrangian energy (L),                from the difference of the kinetic energy (KE) and                potential energy (PE), as L=KE-PE over time, where the                KE is derived from the mass center, linear dynamics such                as forward momentum, and the other mass center                nonlinear, angular dynamics, such as angular rotation of                upper body inertial momentum, and the PE is derived from                the potential changes in gravitational height of the                mass center bouncing up and down through stance phases.            -   Minimizing the use of L energy is an optimization goal                to have efficient transfer between linear and nonlinear                dynamics (Principle of Least Action), while also                minimizing the action (+L) and reaction (−L) over                multiple gait cycles, where the work is reduced with                balancing foot thrust forces as push-up directions, with                gravitational forces as pull-down directions.            -   The interchange between Balanced-Action making efficient                Work-Tracks, is controlled at cognitive and muscle                memory levels, but also involves feedback from skin                sensing of blood flow and muscle pressure, such as the                reduced pressure during the swing phase of the leg. This                is an efficient sensing channel not obvious in                biomechanical models and is the essence of efficient                locomotion.        -   4. The example sleeve described herein incorporates the            spatiotemporal analysis of sensor measurements in            correlation between paired limbs in the lower body, and/or            also in the upper body, but at a minimum it is with the            L-Calf and R-Calf sensing, with RF links used to correlate            the individual work and the Action computations shown next.    -   PST Action and Work Correlated Computations, where the KE of the        swing and thrust gait components and the PE of the stance double        support periods contribute to the Action, and the Work is being        measured during the singular support stance while moving the        swing in a limb motion against momentum and gravity. Example        data is presented in relative units of circumference pressure        scale change, after scaling of the raw data. Note the following        individual and correlated limb discoveries:        -   1. FIG. 13 for 10 sec/10 cycles of calf pressure data, being            shown for both Right (FIG. 13a red color) and Left (FIG. 13b            blue color) calf's sleeves, here with:            -   Vertical zero-crossing dashed lines delineating the                stance phases (rounded sine wave top, pointing up, as                rounded peaks more typical of GRF data), and            -   In-between swing phases (pointing down, as valleys with                more pointed bottoms), as cyclic below the FIG. 13a                data, which also correlate with the Right gait phase                delineation. The double-hump in the stance is paired                with the pointed valley in swing by the opposite leg.                Sometimes this pattern is such a ‘mirror image’ pattern,                they seem subnormal, yet this is a similar observance.        -   2. Another example of correlated data is shown in FIG. 14,            with letter labels on valleys (swing) and peaks (stance),            for R-leg in red color, marked with “R's,” L-leg in blue            color, marked with “L's,” shown for walking at 1 mph for 10            sec (6 cycles, FIG. 14a ,) and running at 5 mph for 10 sec            (8 cycles, FIG. 14b ).            -   Note the ‘double support’ of both legs in stance by the                crossing of the traces above the mean in walking, and            -   Note how the faster running has self-synching of the                swing, to very precise valley ‘ticks,’ while the peaks                are moving relative to each other in time location and                amplitude.    -   Sensor Fidelity Improvements, included changes in electronics,        fabrication of sensors and materials, and placement within the        sleeve, allowing for an inherent impulse response at <1 msec.        Examples include:        -   1. Improvements shown in FIG. 15 for a 2.3-sec/3-stride            example has increased pressure sensor fidelity, with IC            events marked with a dashed circle, which varies slightly in            position in walking, and a        -   2. Re-scaling (red color, upper curve) of this high fidelity            data in FIG. 16, shows an improved linear dynamic range.            Here, the trends of the stance upward peaks and the swing            downward valleys are very precise for the peak and valley            time periods, arguing for a 50%/50% swing to stance peak            time ratio, except at the zero crossing, the ratio in time            is more typical at 23%/70%.        -   3. More simultaneous sleeves, are shown in a further example            of 16 channels of correlated, limb muscle pressures, shown            in FIG. 17, for 2.8 sec/3 gait cycles of data; some data was            truncated in the sampling as a clipped voltage); hence, only            four muscle-aligned, channels are plotted, noted by limb            type and board number abbreviation listed in the title.            -   Note that the thigh data of FIG. 17, has dominate                singular pressure increase from lifting of the lower                calf limb during the swing phase, shown being paired                with calf data (numbers indicate which MEMS module is                measured, i.e., LT3 with LC6 for left correlation, and                RT19 with RC15 for right correlation, with thigh leading                calf swing.            -   Note the correlative alignment of the thigh and calf (RT                lagging to LC in swing; LT lagging to RC in Swing; RT                and LT in precise periods on falling edge; LC has an                amplitude offset artifact that reduced the stance and                truncated the swing; RC has a stance peak reduction with                subsequent cycles; RT and LT have trailing, secondary                peaks.            -   Note also there are individual changes in the stance                (and thigh lifting phases, during the swing data), which                will be corrected with improved electronics in data                sampling, described later.

The elements of Action and Work are correlated as well, shown in a modelrepresentation in FIG. 18 as a 3D (XYZ) function in FIG. 18a , where thetemporal space of the Action from the Lagrangian energy (t-axis alonghorizontal X-axis, with values along the positive “Z-axis up”), iscorrelated from the spatial integration of the Work forces of G and A(x-axis along the “Y-axis into the page” with values along the positive“Z-axis down”). Here, in FIG. 18b , the correlation is computed withinthis overlapping region shown for the 8-cycles of the gait model, but inreality this is just determined by the time change detection synchedwith the spatial changes. The plot of expected PE and KE in FIG. 10 forrunning going to zero at Stance Reversal, StR (i.e., L=small+L, 0, orsmall −L), argues for the ACM dynamic switching to conserve angularmomentum at StR.

In the earlier FIG. 14, the walking and running L-R calf pressurecorrelation examples have considerable peak synchronization, despite theindividual gait cycle variations along the stance and swing correlation.A key distinction shown between the walking and running example, is themore precise alignment in the swing phase of the pressure data duringrunning, with very reproducible patterns. On the other hand, the walkingdata seems to show examples of inter-stride correction of the stancephase pressure useful in retaining Balance, due to L-calf irregularstance pressure causing a correction by the following R-calf sequence ofvery fast off/on force changes (as short dips during the stance periodiccycle). These data imply the very important use of the swing phase inBalance correction, allowing for regular cadence as a fine tuninggovernor of an engine, with the stance phase having less importance tothe gait cycle details, but can provide the ‘course’ power engine inlocomotion to overcome changes. In a sense, these data are “fingerprints” of the individual's locomotion, and can be retained as PSTprovides Balance and Track information for chronological databasemonitoring. These details are made more apparent in the correlation ofAction and Work being applied to this detail through the computationsindicated in FIG. 18.

Detailed Metric Application for PST Data

The concept of using PST in a variety of data collections and analysisover a variety of time scales, emulates from the definitions oflocomotion within standard gait cycle modeling, and the human cognitionand muscle memory neurological processes, as used in psychological andphysical therapy (PT) modeling. The standard gait cycle consists of twomajor phases for each lower body leg, being either stance or swing phasefor one or the other leg, with a short time spent in double leg support.Within this cycle there is a two-step stride process for the L-step toRight-step, and then back to Left-step. This basic time scale is on theorder of 1 sec (1 Hz) in standard walking, with four components each ascomplete 8-period locomotion for the two phases. There are also possiblymore than TO and IC sub-gait time event components, e.g., the roughly 10msec IC events in FIG. 15 for three strides, being on the order of 100Hz data sampling required for sufficient representation of detailedrepresentation. However, there are both locomotion scales below thisstandard scale, being on the order of sub-msec sampling as a micro-scale(e.g., kHz) as seen in EMG neural muscle measurements, and a global, ormacro-scale, being over a few strides for an average locomotionassessment of over 10 sec (0.10 Hz). Thus, long term human Balance andTrack motion as a diagnosis or training tool, on a scale of minutes, tohours, days, or even a year is required for all of the PST applicationspossible (i.e., 60 sec to 32 M-sec, or from 1E-02 Hz to 3E-08 Hz, asshown in FIG. 19). The “sweet spot” for this analysis is along thediagonal of five scales (Micro, Gait Cycle, Mini, Macro, and Mesa), withapplications relating to fine details in locomotion, indicated in thefigure as small time scale applications relating to the body Muscles,and then larger time scales relating Track& Balance, and on intoTraining, Cognition, and Aging. Action & Work correlation analysis isshown for the Mini-cycle scales, because it relates sequences of thegait cycle to longer integrations of B&T, and Symmetry & Efficiency areshown at the Macro-cycle scales, because they relate to longer periodsbetween events on the asymmetry of the gait and the efficiency of theamount of Work produced from the Action of the Lagrangian energy. Here,a finer trend is of interest, and small changes must have significancein the longer term estimates.

Obviously, the variations in locomotion analysis over this micro to mesascale of FIG. 19 covers ten orders of magnitude in time and frequency,which is unusual in normal human analysis, as this is equivalent to agait cyclic period of 1.5 m/sec covering a distance of 1 mm to 104 km(e.g., 5 km/hr for 2000 hr). The process of covering such a large outputof data to analyze in an automated fashion is to analyze events ofscale, based on the precision of the swing phase and stance phasereversals from FIG. 8 and FIG. 10 (StR, SwR), as measured in the PSTexamples. Beyond the sweet spot of the diagonal shown for the GaitCycle, there is an ever expanding development over the trend analysisthat creates an Information Metric Hierarchy, shown in FIG. 19 along thediagonal moving from Diagnosis applications to Treatment and then tolong term Recovery.

FIG. 20 incorporates the scales of FIG. 19, into a sensor processingdesign for many different bands, beginning on the left side with aself-synching of the data sampling at a fixed data rate (f₀) across Msleeves having a unique b^(th) board ID, for a set of many channelsindexed as Mb being feed into the first stage of the data processing.This process is output to the correlation analysis stage for timeintegration (T) at correlation lags (t). The parameters in FIG. 20 areset for eleven bands to cover the space of FIG. 19, and are set for aset of eleven lags within each of the eleven integration times, to coverthe breadth of the trends, and are optimized for the diagonal, 7-11 setsshown on the right, lower side of FIG. 20. Specific parameterconstraints are listed based on early data results.

FIGS. 21a and 21b -1 through 21 b-5 show the algorithm flow to processthe data using the parameters of FIG. 20, as shown in FIG. 21aalgorithms, and in FIGS. 21b -1 through 21 b-5, examples of single calfpressure are shown that span examples of events from 10 msec, <300 msec,<3.5 sec, <100 sec, and >100 sec. In each of these scales, the datacontinues to show synchronized frequency dependence in thenonlinearities of the swing data near the valley, similar to a clockticking, also shown in the spectral plot peaks and phase synchronizationtransitions. The algorithm that operates on this data set, shown in FIG.21a , involves many orders in integration time and time lag correlation.The data examples shown in FIGS. 21b -1 through 21 b-5 are betterdisplayed and marked in two time trends of events for each scale,ranging from msec to minutes, using the swing “tick” sequentialvariations, and the stance peak ridge trends, as shown in blue, red, andbrown markings. The algorithm is centered around the gait time periodfor event detections, using a real time clock in each sleeve (RTC) beingsynchronized with the RF communication exchange for minimizing errors inthe cross/within channel correlations.

With a parameter selection set for the diagonal setting in correlationand integration (τ_(i), T_(j)), the gait cycle events are feed into aparallel processing to compute the B&T products and the A&W sums asintegrations in time and space respectively, with a Buffer Memory tofacilitate a realtime output rate of this processing. In this case thechannel set is based on a left and right calf set of measurements, whichare then merged for distribution in various applications. A higherresolution of the last data example in FIG. 21b -5 at the bottom,lasting for over 10 minute trends, is shown in FIG. 22 in more detail,where the IC event and the peak and valley correlations are shown withtrends over 100's of cycles in both stance and swing peak and valleychanges (FIG. 22a ) and still with an identification of an IC eventtrend (FIG. 22b ) and peak stance trend. Product summary details follow.

Product Summary

The PST technology is based on a precise means of measuring limb musclepressure concurrent with Earth's magnetic and gravitation field angularlocation, and vector acceleration on the body COG and linear momentum(CM), and angular velocity and acceleration of the inertia (ACM). Thehigh fidelity of the pressure sensing allows for the many analysisscales of sampling to not loose long term trends, as would be typical inan averaging algorithm over periodic gait cycles. It is the aperiodiccycle of the swing events which creates this internal locomotion‘ticking’ from both muscle and mental performance. Thus, the pressuresensor measurement circuit and analytic, calibrated scaling removesnonlinear outputs as shown in FIG. 23. FIG. 23a shows a Wheatstone andelectronics design expected to further improve current fidelity dynamicrange, and to operate at over 24 bits of data sampling at 16 kHz rates(vs. current 16 bits, theoretically going from 96 dB to 144 dB) alongtime variations within the three frequency bands shown (LF in 0.05 Hz,HF in 20-1 kHz, and HF in 2-4 kHz, at 16 kHz data sampling). The knownproblem in current Commercial Force Sensing Resistors, shown in FIG. 23bfor translating force to resistance, requires an inverse resistanceanalog scaling (i.e., 1/resistance, shown as “R” in the figure) toremove this nonlinear effect (current commercial designs use a positivefeedback circuit in the electronic operational amplifier circuits), butwere shown to have an increase in electronic noise, causing current PSTwork to use a different approach. FIG. 23c shows an analytic scalingfunction used in software of the data processor shown in FIG. 20, andapplied to data in FIG. 16. This function was determined with precise,calibrated sampling, established across a number of PST sensors andestimated to a R̂−0.9 variation, to retain the PST sensitivity in alinear manner for precise swing and phase data computations.

FIG. 24 shows an example of a female model sitting in a chair in theupper left corner taken as sequence images from a video, attaching thepreproduction sleeve to each leg and then standing up and starting towalk on a treadmill for testing. The images frame through the R-heelstrike at IC through stance, while the left leg is in swing, and thenthe right leg goes through swing while the left leg is in stance, endingwith IC to start the cycle over again. There is also an example of rawdata shown on the computer screen, during data collection, with asingle, 1-second of walking gait cycle data being shown for the rightsleeve of five muscle groups similar to the data shown in FIG. 6. Thelower part of FIG. 24 lists benefits of the PST in a product and variousRF connectivity used by the individual, the trainer, and even themedical practioner, using the various product computation and displaytechnologies that are used in the PST product applications. While thecomputing hardware will change over time, FIG. 25 shows a diagram ofMEMS and PCB construction for an example sleeve construction andfabrication for comfortable sleeve attachment. The FIG. 25 sleeve bandwith multiple individual printed circuit boards (PCB) each integratingtwo 2D MEMs sensors with a single force sensitive resistor (FSR), beingelectronically measured with a circuit like that shown in FIG. 23a . Inparticular, the boards each include a magnetometer, a gravitometer(accelerometer) on an outer side and a force (pressure) sensor on theinner side. An adjustable buckle or Velcro strap tightens the band. Thesensors are connected to a processor (e.g., an ARM digital signalprocessor), which processes the sensor signals for PST metrics of B&Tand A&T, as well as typical gait metrics. RF communication is used forsleeve to sleeve communication for computing these metrics locally(e.g., Bluetooth (e.g., processor-USB to RF transceiver from Targus,IOgear, Sabrent), or using ANT+(to a wrist watch, e.g., TIMEX Ironman),and for longer distances like to a laptop for example, using wirelesscommunication to a computer via a radio module such as from the XBEEfamily available from Digi International, or a Qualcomm Life Internetlink to the cloud.

Other example constructions for sensing the various parameters describedherein are shown in U.S. Pat. No. 7,610,166 and U.S. Patent PublicationNo. 2011/0208444 (the contents of each of which are incorporated hereinin their entirety).

Specific applications for the PST in some of the connectivity shown inFIG. 24 include examples of application users: for training inProfessional Sports (5000 players in ball sports), Professional HorseRacing (5000 horses, not including instrumenting the jockeys andriders), ACL injuries in diagnosis in pre-Op and post-Op, treatment, andphysical therapy (200K surgeries per year, with some being a repeatoperation), stroke, back disorders, brain disorders, Elderly care, whichhas shown the first signs of dementia (i.e., cognitive model: braincommanding system function, muscle memory executes action—measuringmuscles measures brain), including Alzheimer's disease, may not be afaulty memory, but problems with balance and walking, according to a newstudy by UWA, Group Health Coop, found senior citizens who participatedwere three times less likely to develop dementia if they maintainedtheir physical function at high levels (total users at >10M), and all ofthe semi-professional, collegiate, and even high school for training andtesting for predisposition in ACL injuries from gender (females have aworse Q-angle). The PST can be tailored for these applications withminor software changes, including for the long term analysis examplesshown the trends of FIG. 19.

PST System Concept

The described systems, methods, and techniques may be implemented indigital electronic circuitry, computer hardware, firmware, software, orin combinations of these elements. Apparatuses and systems embodyingthese techniques may, for example, include appropriate input and outputdevices, a computer processor, and a computer program product tangiblyembodied in a non-transitory machine-readable storage device forexecution by a programmable processor. A process embodying thesetechniques may be performed by a programmable processor executing aprogram of instructions to perform desired functions by operating oninput data and generating appropriate output (e.g., visual output, auraloutput, and/or tactile output). The techniques may be implemented in oneor more computer programs that are executable on a programmable systemincluding at least one programmable processor coupled to receive dataand instructions from, and to transmit data and instructions to, a datastorage system, at least one input device, and at least one outputdevice. Each computer program may be implemented in a high-levelprocedural or object-oriented programming language, or in assembly ormachine language if desired; and in any case, the language may be acompiled or interpreted language. Suitable processors include, by way ofexample, both general and special purpose microprocessors. Generally, aprocessor will receive instructions and data from a read-only memoryand/or a random access memory. Non-transitory storage devices suitablefor tangibly embodying computer program instructions and data includeall forms of non-volatile memory, including by way of examplesemiconductor memory devices, such as Erasable Programmable Read-OnlyMemory (EPROM), Electrically Erasable Programmable Read-Only Memory(EEPROM), and flash memory devices; magnetic disks such as internal harddisks and removable disks; magneto-optical disks; and Compact DiscRead-Only Memory (CD-ROM). Any of the foregoing may be supplemented by,or incorporated in, specially-designed ASICs (application-specificintegrated circuits), logic circuits, gate arrays and the like.

As discussed in detail above, one of the most important activitieshumans do is to think about what they observe from their “sensors”, andhow they use that information in general to move on with their life. Animportant aspect of this is self-controlled sensing, the ability to moveunder own power and have the freedom to go where we want, do what wewant when we get there, and see/hear what we want as sensory perceptionof our observations from a better observation point. This is the dynamicof human locomotion, and is fundamental to our thinking and our life'sdesires.

Humans move like a locomotive traction-engine pulls a train, by creatingfriction forces on the ground with our feet, because that is the onlyplace we can change our mass location by exerting a force. But becausewe stand upright, we have to place our feet in front as steps, to moveanother point down the path of our intended direction, as mentallyreaching forward. The connection between the human brain and our sensorperception for locomotion is one and the same: no sensing means nolocomotion; no-locomotion means no-brain stimulation. We Locomote byfalling down in the general direction we want to go, but in order to notend up on the ground, we place another foot in front to catch us;otherwise we would end up crawling. This is a rather crude method, butit works for every human body, and locomotion is guided by all thesensors as a unitary action. Our feet in bipedal walking or runninglocomotion, surprisingly are not very sensitive to what shoes we wear,because the body is so adaptable.

While human bio-mechanic models today are extremely complex, we are justbeginning to understand and predict how we do it. It is well known thatthe human is self-synchronizing in a manner that the brain just guidesthe locomotion goals and the sensing make corrections and change localdirections to our muscle memories. But the muscles also tell what isgoing on by the feedback pain of steps, and twisting and turning,stretching other body action that is perceived. This upper body dynamicthrows the legs to where they need to be, as a corrected trajectory andnot a rifle precision. Thus, errors in the brain appear as errors inlocomotion and errors in the locomotion can appear as problems in thetraction engine to create frictional forces.

Thus, the best way to understand locomotion is to measure how the forceson the ground that make the friction with the feet are created andchanged. The example systems and methods described above measure thecalf muscles which are a major contribution to the foot thrusts inlocomotion. The precision of this locomotion is tied to the precisemanner that the swing of the leg in planting the foot on the ground iswhere necessary precision is applied and corrected as needed.

The example systems and methods described herein enable the combining ofMEMS 3D gravitational measurement (G) and magnetometer measurements (B),with pressure (P) measurements in spatiotemporal integrations forestimating action (A) and work (W) using event detections of peak Stanceand valley Swing events, along with ‘triangular’ curve shape areaestimation, scaled relative to “zero” P measurements, and estimatingBalance and Track transitions on ground contact for dω/dt=0.

The example systems and methods also enable distinguishing between threemodes of PST pressure sensing during locomotion based on feet touchingthe ground, namely, Two Feet, as an in stance on both feet (double limbsupport), and while extending a force moving to one foot, e.g., hittinga ball; One Foot, as a) the hitting impulsive action creates anunbalancing, reactive force, or b) when applying a pushing force, whichis less impulsive in time, it creates a direction for continued forceapplication, e.g., throwing a ball on one leg, or having contact withanother large mass body; and Zero Feet, as in regaining balance onreturn to track of one or two feet that must dissipate or redistributethe angular momentum.

The example systems and methods also enable incorporating the modelingof locomotion, with the energy absorption and generation model, withinthe Action and Work efficiency metric under these three modes, wherebythe transfer of angular momentum (ACM) changing Balance is correlatedwith the transfer of linear momentum (CM) changing Track such that thesetransfers use the PST identification time of maximum swing extensionforce (maximum centrifugal force), and these transfers use the PSTidentification time of the minimum stance foot-step force (trailing zerocrossing from peak pressure).

The example systems and methods also enable periodic and aperiodic timeboundary detection using HOS correlation on PST data.

The example systems and methods also enable combining the B&T and A&Wcomputations in a PST sleeve localized manner, in order that the twopaired PST parts can be reconstructed as a complete, correlativeestimate (e.g., R-Thigh to L-Thigh, R-Calf to L-Calf, R-Thigh to R-Calf,L-Thigh to L-Calf, and further upper body limb intra-correlation pairingin a similar manner, inter-correlation pairing with lower body limbs,computations of symmetry, computations of efficiency, and computation ofoptimized locomotion for local visual, aural, or electrical stimulusfeedback.

The example systems and method also enable combining multiple PST modulemeasurements on the same limb sleeve to separate angular circumferencecontributions from local muscle pressure, as a further metric in musclephysiology for determining how the locomotion structures and effectorsuse energy as net cost of transport, defined as the energy needed tomove a given Track distance, per unit body mass.

The example systems and methods also enable calibration of PST using asimple jump after attaching the sleeves to the limbs to start the systemfrom a sleep mode, perform an alignment with the magnetic North and jumpagain, and then perform a 90° rotation to magnetic West, followed by thelast jump before beginning movement. Here, the jump aligns the GRAV MEMSwithin all PST modules on all bands, and then the rotation does the samefor the MAG MEMS, and finally the last jump is compared to the first inthe PRES MEMS to calibrate all the sensors in relative location at three“step” in double support mode events, which are a signal to theprocessing to derive calibration parameters before processing data.These parameters are updated depending on the application, or stored andreused at the control of the user.

The example systems and method also enable integration in PST of forceamplifier to FSR as a directly attached puck to resistive sensingmaterial. This is used in combination with the built-in backing materialof the sleeve and the buckle adjustment to achieve a comfortable and yetsnug fit.

The example systems and methods also enable combining local PST PCBMEMSW gravitational measurements (G) and magnetometer (B) 3D vectormeasurements with pressure P, to estimate foot thrust force A, followingthe equations in the figures and the selected time constants forintegration and lag defined by each.

The example systems and methods also enable combining B, G for pairedthigh and calf PST sleeves to estimate a dynamic “Q-angle,” defined overa gait period from stance into swing back to stance separately for eachleg, as the 3D MAG location of each limb, with motion corrections.

The PST described herein provides application specifics for the dataprocessing algorithms as typical constants:

-   -   1) Sensor type and placement.    -   2) Information extraction details; algorithms, processing, time        scales, spatial integrations, data flow parameters for        change-detection algorithms.    -   3) Information display and database organization and analysis        tailored for each application.    -   4) Computing of correlation in sensor dynamics with both        second—order and fourth-order (in excess as a higher-ordered        statistic, HOS). These are used in:        -   Additional modeling of combined limb correlations used in            algorithms defined by second order:            -   i. calves/thighs, forearm/biceps,            -   ii. shoulder-(collar-bone (clavicle), humerus (the upper                arm bone), and scapula (shoulder blade), rotary cuff                joint)            -   iii. pelvis-(hip-bone (coxal-bone), sacroiliac-joint,                sacral-sromontory; hip joint)        -   Additional modeling of combined limb correlations used in            algorithms defined by fourth order:            -   i. upper-body/lower-body,            -   ii. twisting-spine (ACM) foot-placement (CM)            -   iii. joint centered motion in elbow/knee, shoulder/hips.    -   5) Gait applications in physical therapy, with the addition of        the swing measurement and L-R leg correlation analysis from        using PST metrics.    -   6) ACL injury in preventive medicine applications and training        for predisposition to injury, using PST metrics.    -   7) Sports training and equipment development applications, using        PST metrics.    -   8) Pre-/post-operative diagnosis, treatment, and recovery        assessment, using PST metrics.    -   9) Mental impairment applications (stroke, Alzheimer's, brain        damage), using PST metrics.    -   10) Elderly and aging applications (foot drop, feet stuck to        floor, unbalance, and asymmetric gait), using PST metrics.

Thus, the technology described herein can provide enhanced sportsperformance coupled with injury avoidance as continuous, two-beat gaitinformation from paired leg measurements. For example, as describedherein, PST provides paired sensors in sleeves on lower body calves.Calf sensors with local feedback can, for example, coordinate a runner'sstride to have an efficient pace and warn of potential weakening thatcould lead to injury. In addition, the technology described herein canprovide for new measurements such as precise proprioception muscleaction measurements. Such measurements can be used, for example, toevaluate central nervous system disorders.

The technology described herein can be widely utilized by professionalsand amateurs in sports training and risk assessment including racing,jumping, hiking; team and individual sports including, but not limitedto, basketball, baseball, lacrosse, hockey, soccer, tri-athletics, golf,tennis, football, and the like, at all levels (e.g., high school,college, professional, recreational) and including all styles (e.g.,bike, skateboard, ski, run, swim, barefoot).

The technology described herein is also useful for practitioners inorthopedic ACL surgery, diagnosis, rehabilitation, and recovery, as wellas researchers in locomotion and gait analysis, health care workers,medical elderly care professionals. The technology can also be used incentral nervous system (CNS) diagnosis, monitoring, and treatment ofconditions including, but not limited to, dementia, fall down, stroke,spine, lower back, Alzheimer's disease and the like.

In the past, locomotion analysis used photographs, force plates, shoepads and video to collect data to review and understand how people move.This is technologically equivalent to filming a car's tires to determinewhere it is going. The technology described herein provides on-boardinternal locomotion sensing that is fast, inexpensive, providesreal-time feedback, can be used outdoors, and measures both stanceground reaction force (GRF) and swing phase (free leg motion) withoutcameras.

With reference to FIG. 26, stance only measures four gait parts ofInitial Contact (IC), Flat (full) Foot (FF), Heel Off (HO) and Toe Off(TO). The technology described herein provides measurements that enablean understanding of how people move with continuous swing phase fromstance phase as a single stride evaluation. The technology measurescontinued muscle contraction after TO, and proprioception feedbackcorrection before Swing Reversal Transition (SwRT) before IC.

With reference to FIG. 24, the technology described herein providesunique measurement of swinging leg motion, measuring muscle and brainactivity and connects to existing network/readout devices to provideinformative health metrics. An example implementation includes RF-linkedsensors placed on belt and the sensor count may vary depending on theapplication from amateur sport to orthopedic diagnostics and physicaltherapy. Product categories include sports, ACL injury PT-treatment,elderly, ‘game exercise’, navigation and the marketplace applicationcorresponds to a software modification relating to the number of sensorsand readout information. The technology utilizes correlation betweenpairs of sleeves attached to thighs, calves, forearm and/or biceps andwatch/laptop RF connectivity, for example, may provide connection bytelecom to cloud computing, server historic analysis, database mining,pre/post op medical comparison, trainer monitoring of team players andthe like.

The technology described herein uses a model to interpret themeasurements and provide user feedback in real time. The modelincorporates correlation analysis between paired limbs using an RF link(e.g., computation may occur in a watch or cell phone or other computerdevice). The technology can tie all data to a singular event in stridefor each limb, being the SwRT (TSwR) “tick” to relate all stride data inan absolute time reference. The technology uses physically modeledAction and Work to estimate efficiency and incorporates estimates ofupper body balance as the difference in summed 3D vectors for each legin 3D gravity (G), to that of the estimated 3D leg thrust force (A)using a calibrated sleeve contraction/expansion as amplitude and thevector angle from a calibrated magnetic leg shank angle.

With reference to FIG. 27, both left/right calf data shows correlationof stance synchronization transfer and SwRT valleys in half-strideperiod synch, with zero-crossings of data marked with dashed line.Mid-fidelity PST measures stance/swing phase with times with swingreversal adjustments (indicated in dashed ellipse) of minimum datavalley, and proceeded with a bump corresponding to the proprioceptionfeedback adjustment.

As summarized in FIG. 28, the model uses an about center of mass dynamic(ACM) from angular momentum of twisting spine moving upper bodyshoulders to swing arms to ‘pump’ lower body pelvis to swing thighs,translating the swinging momentum to a linear momentum for the center ofmass (CM) forward motion with a track of footsteps with minimum friction(e.g., as in a piston linear thrust transfer from a cam rotation) tominimize side friction forces and maximizes forward locomotion forces.

With reference to FIG. 14, further examples of left/right stancetransfer and swing dynamics show correlated SwRT with proprioceptionthat changes in stride time period between walking and running,accompanied with a different foot thrust force shown with running (aswell known), but the detail shows muscle changes during the stride to beused in Balance and Track computations, noted by peak stance amplitudes,and minimum swing amplitudes (valleys).

With reference to FIG. 29, vector force equations as G and A differencesshow the estimation of a single sleeve on the left, balancing on one legin the swing mode, where the ACM is creating an angular momentum forbalance, and with sleeves on each calf on the right in a double stancemode, which is only 20% of the time. The equations show that the Centerof Pressure (COP) for both L-left, R-right legs (COPL, COPR) dynamicallyin time (t) balance the Center of Gravity (COGL, COGR), to continuouslymaintain balance for COP=COG. This is a dynamic stability between theforces and the angles of the body, which includes the balance of theangular momentum of ACM switching between the swing and the linearmomentum CM establishing a stance balance.

With reference to FIG. 30, proprioception is a feedback from the limbposition and speed dynamics up the spine to the brain, used incorrections controlling the locomotion dynamics just before the SwRTpoint valley, as a small peak ‘bump’ as an expansion in the sleevepressure, where this balance is a parametric amplifier synchronizationof the upper body and lower body pumping, which is seen in the sleevedata.

With reference to FIG. 31, the model uses a graphic dynamic to determinebalance and track in another way, where the horizontal axis is the legangle from the hip to the toe, and the vertical axis is the Leg anglebeing upright at 90 degrees. The eight (8) phases of the gait stridecycle are also indicated for one leg, and an example of distorteddynamics is shown in the thin line, particularly in the upper leftcorner of the plot, just before the IC part of the cycle, here withconservation of angular momentum (L) defined in this model as a torquefree (no friction) period in time (t) as (dL/dt=0). Any deviation hereis a reduction in efficiency, so this data derived from the sleevemeasurements is another means of developing metrics of efficiency inmomentum transfer from linear thrust forward to angular rotation inswinging the leg.

FIG. 32 provides details of the proprioception muscles involved inlocomotion, showing that a major part of these dynamics are part of thePST metrics in CM changes and calf muscle stretch.

With reference to FIG. 33, using a force sensor placed in a runner'sshoe at the heel and toe, data is collected to show the precisemeasurement with the sleeve data, of when the A force of COP is applied,without using a force plate and treadmill. The four stance phases of thegait cycle are accurately indicated, and the extended pressure shownduring swing, just after TO is the retained muscle contraction of thesleeve data for having a better inertia to swing the leg forward at afaster manner; this detail is so quick that it is not observed in thecamera dynamics used by gait labs and is a unique discovery. Theproprioception bump is clearly shown for three steps, and this precisionis combined with adjustments in SwRT to have an almost zero angularmomentum at IC when the foot returns to the ground as the next IC step.The pressure sensor on the foot is shown in the blue/green positions andthe sleeve data is in red.

FIG. 34 shows details of the proprioception modeling indicating how thePST sleeve can be part of detecting errors in this feedback fromdiseases of the spine and brain disorders, sensory receptors, peripheralnerves, dorsal roots, and central nervous system pathways, which canincorporate a feedback shown to the nerves in the skin for signaling thebrain to improve gait disorders.

FIG. 8 shows the specific muscles being measured in comparison with thenonlinear aspects with a model of the gait being dominated by the rectusmuscles during the unique swing phase measurements, and how this dynamicis tied specifically to the energy absorption and generation about theSwR Time (number 4 shown with an arrow).

In FIG. 35, the relationship between the SwR time (TSwR) and the IC time(TIC), bounding the detailed part of the whole gait cycle, where themomentum transfer must be conserved during the short time the foottouches the ground (less than 10 msec), with the whole event beingcaptured with a total time of 20 msec, as a useful metric for each stepmaking a Track of foot paths.

In FIG. 36, the same full cycle is shown with a very precise marking ofthe events during the cycle, of linear slope changes (SC) peaks (P) andvalleys (V), comprising 25 events to characterize the stride within theabsolute time of the SwRT. Energy absorption (Ab) and generation (Gen)are also indicated along with the typical eight (8) parts of the 1-seccycle (shown with vertical marks at 0.1 sec), with a fidelity ofindividual muscle changes on the order of full-width peak and valley ofonly 30 msec, and slope changes in under 10 msec, as a series ofreproducible data every second with a fidelity of below 1%.

In FIG. 37, both thigh and calf bands are shown to indicate how bothmuscles show the thigh-lifting (T) as a peak and the calf (C) swingingas a valley phase in/out of synch (for L/R sides, and C/T sleeves).

FIG. 38 shows a similar set of data as in FIG. 37, but here all sixteen(16) sensors are used, with a very precise timing of the four musclemeasurements on the 4 limb locations demonstrating aself-synchronization in the gait cycles.

FIGS. 22(a) and 22(b) show a detail of the gait cycles shown over 100strides shown various scales of variations in trends in the IC locationamplitude, and the peak stance pressure and valley swing extension. Thisis useful data to archive for use in monitoring patients.

FIG. 39 shows PST data event time scales, with short events becomingscaled to be part of a larger trend, from 10 msec to 5 sec to 5 min.

FIG. 19 shows a categorization of data scales using the data analysis ofearlier figures, but here being “rolled” up in the analysis for decadescales of various cycles shown in red, to trend analysis output frommuscle to Track and Balance to Training in Action & Work Efficiency, andCognition in Symmetry & Efficiency, as well as the longest scale is forobserving Aging.

FIG. 40 shows a means of transmitting the data stream of themeasurements using a Bluetooth RF link to a gate way to the telecom,such as from a cell phone, and on into the cloud, where the data isplaced on a server for the medical and sports professionals can analyzethe results across historical trend and population or team trends.

FIG. 41 shows an example device package as a standalone device, to beplaced inside a sock or on a sleeve band for multiple calf measurementsor just one, where the unit is battery powered using an inductivecharging and also has onboard processing, such that the correlation ofthe paired sleeves involves just comparing the peaks, slopes and valleysfor correlation.

FIGS. 2 and 18(a) and 18(b) highlight various unique aspects as aproduct of strap-on sleeves to monitor calf muscles for:

a. correlating left-right leg gait stance & swing phases

b. ANT+ to watch to display information

c. information provides real-time, graphic user feedback

d. corrections to run faster with less injury

e. health improvement in efficiency and symmetric balance

f. precursors to potential injury prevention

FIG. 42 shows the unit data analysis with RF connectivity, set up forvariable integration and analysis time scales, between two sleeves andbetween the watch or cell phone into a longer RF telecom connectivity.

FIG. 43 show an example of unit data analysis with parametric control ofthe processing for many time scales with connectivity for an eight-unitcalf correlation example.

The systems and methods described herein are described in connectionwith certain non-limiting example embodiments. The following claims arenot limited to these example embodiments, but on the contrary, areintended to cover various modifications and equivalent arrangements.

I claim:
 1. A system comprising: one or more sleeves, each configuredfor attachment to a leg and comprising a pressure sensor, anaccelerometer and a magnetometer; a processor for processing sensorsignals from the pressure sensor, the accelerometer and the magnetometerto estimate action (A) and work (W) using event detections of peakstance and valley swing events associated with leg movement, wherein theprocessor detects small changes during swing from brain feedback ofproprioception sensing of the lower body component locations andmovement dynamics to adjust the swing reversal behavior with feedbackfor a minimum off-unbalance and use of friction in stepping forward witheach stance step.